抽象的

Ensembles of First Order Logical Decision Trees for Imbalanced Classification Problems

M.Manjula , T.Seeniselvi

The Imbalanced class distributions are frequently encountered in real-world classification problems. The Ensembles classification based on decision tree classification learning is widely used for commercial and medical domain. This issue can be solved by high dimensional ensemble classification based on First order logical decision tree method by increasing the competitive performance. The proposed work is tested with KEEL datasets with different categories. The Data preprocessing methods (Sampling process) method aims to balance class distribution through the random elimination of majority class examples and then Splitting decision tree algorithms generate tree-structured classification rules, which are written in a form of conjunctions and disjunctions of feature values. Bagging based ensemble method increasing the number of minority class instances by their replication and final method is the First order Logical decision tree (FOLD) method which is used to find the variation along with conjunction 0 to 1. Experimental results across many class-imbalanced data sets, including BRFSS, and MIMIC data sets from the medical community and several sets from UCI and KEEL are provided to highlight the effectiveness of the proposed ensembles over a wide range of data distributions and of class imbalance.